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In a way, everything we read and listen that is new to us is News. In the era of information, we are submerged in the sea of news, on paper, in the email box, on TV, on our mobile. Everything is trying to grab our attention. What are the most significant changes in News industry: Moving from Print to Digital: Digital technology is hardly the disruptive force in print media—it is the driving force that shapes content creation and distribution. Hearst Magazines’ US digital operations account for more than 30% of overall US profitability. The New York Times Company has set a goal of doubling its total digital revenue, including advertising and subscriptions, to $800 million by 2020. The company announced the addition of 67,000 digital subscribers in the first quarter of 2016, bringing its total to 1.2 million The P/L is Falling for the Traditional News Providers：While in print media, there is a cost associated with every issue (print and distribution). New York Times quarterly report sugges…

Blockchain can often associated with 5 words “decentralized”, “nodes”, “robust”, “public” and “incorruptible”. As Blockgeek puts in its blockchain 101 article: Decentralized: Block chain is first and foremost a decentralized technology. The term can be found in the organisation, government, management and even in purchasing. Comparing to a traditional transaction system that has a centralized clearing authority, decentralized system replicate the ledger multiple times in each unit of the system and reconcile constantly. It is like how Google Doc works comparing to Microsoft documents. The traditional banks maintain consistency by locking access (or reduce balance) while they complete the reconciliation, it is how a centralized system works. While with Google Doc, all changes are made at the same time and updated, with the document never denying access at any time. Nodes: nodes are computers connected to the blockchain network using a client tat perform the task of validating and repayin…

Machine Learning is undoubtedly one of keywords for 2016 and will continue to be in 2017 and onwards.

3 familiar cases that anybody will be able to associate with:

Case 1: search engine such as GoogleCase 2: social media feed such as FacebookCase 3: mobile keyboard text suggestionsYou can find thousands of links on machine learning definition but knowing the meaning is not helpful. What I found valuable is to know what enabled machine learning, or in other words, what machine learns from, and how it is used now and in the future in different industries.

Throughout this year, I aim to understand how machine learning works and how it will impact us in different industries.

First, let us understand what are the fundamentals that enabled machine learning, or in other words, why now?

To summarize, 4 elements are essential:

Cost to Store Data: I will credit this as the No 1 element for Machine learning, throughout the past decade, Cost of manufacturing hardware continue to reduce, while framewo…

Machine learning to the employment has been a topic in debate. Darrell West, in his paper titled "What happens if robots take the jobs? The impact of emerging technologies on employment and public policy” suggested a list of actions government should take to ensure people whose job has been replaced by machines can live a decent live. The general sentiment seems to suggest a turbulent era as work force transform.

Growing up in China during the time of State owned enterprise reform, I had real experience living through the time of large group of people being laid off because the jobs were suddenly gone.

My parent’s generation had to learn new skills for a completely new industry at their 40s and 50s. Few of them made it and even became millionaire, many of them didn’t and the family suffered a lot. I followed the news of Detroit Car manufacturing industry lapse and it shows familiar traits. The fact is, jobs come and go all the time, employee as a group will constantly adapt while …

Sales activities in many organizations are manual and inefficient. Sales went into the first call or meeting with little knowledge of the client or why the client contacted them in the first place. Their pitches are not relevant so they lose the client’s attention quickly.

Machine learning can help sales in 3 ways:

Improve Lead Quality

One of the most direct and rewarding way sales team can leverage on data is lead optimization. CRM systems provides advanced lead scoring functionalities which rates a prospect on their action (if it is a Saas business, how they interact on the company website and if a manual sales process, how consumers react); background data (e.g. company size; revenue; consumer demographics) and many other factors.

Per Harald Borgen in his article “Boosting Sales With Machine Learning” explain how they use natural language processing to qualify leads. It uses FullContact API to read description of millions of companies from which it got a full list of company inform…